In reference to the Inferential Statistics, which of the following is NOT correct?

A statistical hypothesis is a statement about the population parameter which may be true or false.

This is correct. A hypothesis is a claim about a population parameter (such as mean, proportion, etc.) that can be tested to determine if it's true or false.

The standard deviation of the sampling distribution of a statistics is known as its standard error.

This is correct. Standard error is defined as the standard deviation of a sampling distribution. It measures how much sample statistics vary from sample to sample.

If sufficiently large random samples with replacement are drawn from a population, then the sampling distribution of the sample means approaches a binomial distribution.

This is NOT correct. According to the Central Limit Theorem, when sufficiently large random samples are drawn from a population, the sampling distribution of the sample means approaches a normal distribution, not a binomial distribution.

In a statistical hypothesis testing, a Type-I error is made when null hypothesis is rejected when it is true.

This is correct. A Type-I error occurs when the null hypothesis is rejected when it is actually true. This is also known as a false positive.

The answer is Option 3, as it incorrectly states that the sampling distribution of sample means approaches a binomial distribution, when it actually approaches a normal distribution according to the Central Limit Theorem.